Hybrid journal (It can contain Open Access articles) ISSN (Print) 1740-8865 - ISSN (Online) 1740-8873 Published by Inderscience Publishers[447 journals]
Authors:J.V.N. Lakshmi Pages: 3 - 18 Abstract: Machine learning algorithms are used to parallelise the workloads. To achieve paramount performance required parameters are tuned in the algorithms. The jobs are implemented using machine learning techniques using various parameters. The performance is examined by executing various features and verifying time constraints depending on the assignments for a cluster. The attempt is made to obtain minimum execution time using python language for implementing machine learning algorithms. Supervised and unsupervised techniques of machine learning algorithms are used differentiating the performance evaluation and time efficiency. Linear regression, logistic regression and K-means clustering techniques are used to evaluate the data analytic jobs. This implementation reveals the best performance of supervised algorithms over unsupervised for data analysis. This paper is an attempt made to analyse the machine learning techniques and evaluates the timer feature on various methods irrespective of supervised or unsupervised. Keywords: machine learning; linear regression; logistic regression; K-means; EM Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 3 - 18 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091584 Issue No:Vol. 17, No. 1/2 (2018)
Authors:Manish Kumar, Cherian Samuel Pages: 19 - 41 Abstract: Integration of renewable distributed generation technology with radial power distribution system is rapidly growing. Among the number of available renewable energy sources, the role of wind energy in the power sector is very important. In this work, authors have estimated the wind energy potential at Banaras Hindu University (BHU), India. Estimation of wind energy potential depends on statistical analysis of wind speed distribution, which will give us the best-fitted probability density function of the desired location. For statistical analysis of wind speed distribution, authors have taken hourly data of BHU centre from the source of National Renewable Energy Laboratory (NREL), USA for the 12-month period. A statistical R-programming language tool has been used for the computational work. In this work, authors have considered Weibull, Gamma and Lognormal probability distributions for getting best-fitted distribution. After the analysis, result shows for the given wind speed data of BHU area, Weibull distribution is the best-fitted one. Keywords: statistical analysis; wind speed; wind energy potential; renewable energy; renewable distributed generation; probability distribution; goodness-of-fit Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 19 - 41 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091585 Issue No:Vol. 17, No. 1/2 (2018)
Authors:Chithra Selvaraj, N. Bhalaji Pages: 42 - 54 Abstract: The handwritten text reader is designed to help the visually impaired listen to an audio read-back of printed and handwritten scanned text. A hand-held page scanner is used to scan the text to be read. The image from the scanner is sent to the application in the paired Android phone over Bluetooth. An open source optical character recognition (OCR) engine, Tesseract is used to extract the text from the image, and this extracted text is converted to speech. Tesseract OCR engine is further trained to recognise handwritten text for a specific user. This OCR engine is trained with handwritten datasets. In addition to English, the application supports two regional languages - Hindi and Bengali. Keywords: handwriting recognition; Tesseract; OCR; optical character recognition; text to speech; Android; visually impaired Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 42 - 54 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091586 Issue No:Vol. 17, No. 1/2 (2018)
Authors:K. Radhika, S. Varadarajan, Y. Muralimohanbabu Pages: 55 - 69 Abstract: Satellite image classification is an imperative system utilised as a part of remote sensing. Primary data of extraordinary significance to different difficulties can be acquired straightforwardly from Land-cover observation. Different information partitions inferred by various clustering methods can be gathered into a new solution by cluster ensembles. Supervised iterative expectation-maximisation (EM) method can be initialised by cluster ensemble based strategy which will be examined in the paper. The performance of clustering of the proposed method is compared with individual clustering of the ensemble for medium resolution and a very high spatial resolution images. The accuracy measurements have done with different test points. The state of art techniques are giving less accuracy and are not well defined. This paper will explore the possibility of all accuracy parameters with supervised classification results. The accuracy parameters are well tested and compared in this paper with various start of art techniques. Keywords: classification; image; satellite; ensemble; resolution; accuracy; kappa; multi spectral; classifier; validation Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 55 - 69 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091587 Issue No:Vol. 17, No. 1/2 (2018)
Authors:N. Sivarami Reddy, D.V. Ramamurthy, K. Prahlada Rao Pages: 70 - 88 Abstract: This paper addresses simultaneous scheduling of jobs, machines and tools considering tool transfer times (TTTs) between machines, to generate best optimal sequences that minimise makespan in a multi-machine flexible manufacturing system (FMS). Performance of FMS is expected to improve by effective utilisation of its resources, by proper integration and synchronisation of their scheduling. Aim of this paper is to address joint scheduling of jobs, machine and tools in an FMS consisting of machines, central tool magazine and tool transporter considering TTTs. Three nature-inspired algorithms namely Symbiotic Organisms Search algorithm, Crow search algorithm and Flower pollination algorithm, are proposed for solving joint jobs, machine and tool scheduling problems considering TTTs between machines with minimum makespan as objective. The proposed algorithms are numerically tested on various problems and results are compared. The results show that FPA algorithm yields better results for simultaneous scheduling of jobs, machines and tools considering TTTs between machines. Keywords: flexible manufacturing systems; simultaneous scheduling of jobs; tools and machines; tool transporter; symbiotic organisms search algorithm; crow search algorithm; flower pollination algorithm Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 70 - 88 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091588 Issue No:Vol. 17, No. 1/2 (2018)
Authors:Nadana Ravishankar, R. Shriram Pages: 89 - 97 Abstract: The advent of social media has enabled people to easily and publicly express their ideas on a movie/product in such a way that it reaches millions of people within no time. This research aims to implement a tool that would be helpful in predicting the genre of the movies as perceived by the audience through linguistic rules and natural language processing (NLP) tool kit. This paper focuses on development of rule-based sentiment categorising tool for Tamil tweets and a tool has been developed using Python and NLP tool kit. Furthermore, a model is designed to determine the opinion along with genre classification of Tamil movies. For this work, a set of genres are selected from Tamil movies with public tweets based on sentiment analysis. We find that the tool classifies the genre of a particular movie provided by user tweets and validated our approach with domain experts and baseline models. Keywords: Tamil tweets; sentiment analysis; natural language processing; NLP; grammar rules; data mining Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 89 - 97 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091589 Issue No:Vol. 17, No. 1/2 (2018)
Authors:N. Bhalaji, K.B. Sundhara Kumar, Chithra Selvaraj Pages: 98 - 108 Abstract: Feature selection methods are deployed in machine-learning algorithms for reducing the redundancy in the dataset and to increase the clarity in the system models without loss of much information. The objective of this paper is to investigate the performance of feature selection methods when they are exposed to different datasets and different classification algorithms. In this paper, we have investigated standard parameters such as accuracy, precision and recall over two feature selection algorithms namely Chi-Square feature selection and Boruta feature selection algorithms. Observations of the experiments conducted using R studio resulted around 5-6% increased performance in above said parameters when they were exposed to Boruta feature selection algorithm. The experiment was done on two different datasets with different set of features and we have used the following five standard classification algorithms - Naive Bayes, decision tree, support vector machines (SVM), random forest and gradient boosting. Keywords: classification; feature selection; Boruta; Chi-square; ensemble classifiers Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 98 - 108 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091590 Issue No:Vol. 17, No. 1/2 (2018)
Authors:Prajith Prabhakar, H. Vennila Pages: 109 - 122 Abstract: Microgrid is a low voltage grid which is subjected to disturbances. The distributed energy resources are connected to the LV bus by means of power electronic converters. The power quality issues are analysed with model predictive control (MPC) algorithm in both - grid connected and islanded modes. But the islanded mode is analysed separately. A new model is suggested for the islanded mode with non- linear loads. Predictive control based distribution static compensator (DSTATCOM) injects voltages at the point of common coupling (PCC) in the microgrid. Reactive power compensation and mitigation of harmonics are done with the help of new proposed model. The principles and the proposed algorithm are verified by Matlab/Simulink simulation. Simulation results suggest that this control method can decrease the power quality issues and increase the microgrid stability. Keywords: power quality; active power control; microgrid; predictive control; distribution static compensator; DSTATCOM Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 109 - 122 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091591 Issue No:Vol. 17, No. 1/2 (2018)
Authors:P. Febina Beevi, T.K. Sunil Kumar, Jeevamma Jacob Pages: 123 - 135 Abstract: This paper addresses the problem of design of a two degree of freedom (2-DOF) PID controller for time delay systems. The proposed approach is to design 2-DOF PID controller using a novel method which combines model order reduction, approximate model matching (AMM) concepts as well as optimisation techniques. The conventional PID controllers are usually in 1-DOF structure. The problem of finding the parameters of the 2-DOF PID controller is formulated as that of obtaining the solution of a set of non-homogeneous linear equations. The proposed method not only ensures the stability of the closed loop system with a 2-DOF PID controller but also satisfies the required performance criteria. The developed method does not pose any restriction either on the order of the model or on the structure/order of the controller transfer function. Moreover, this method is computationally simple and easy to implement. Simulation results demonstrate the effectiveness of the proposed method. Keywords: approximate model matching; model; model order reduction; non-homogeneous linear equations; optimisation techniques; performance criteria; stability; time delay; transfer function; 2-DOF PID controller Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 123 - 135 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091592 Issue No:Vol. 17, No. 1/2 (2018)
Authors:Sumanta Bhattacharyya, Manoj Kumar Mukul Pages: 136 - 152 Abstract: The requirement of an effective online processing algorithm becomes very vital to fulfilling the demand of the low-cost brain-computer interface (BCI) system. The authors proposed a very first and robust unsupervised machine learning algorithm, for the real-time classification of movement imagination. The reactive frequency band (RFB) of the individual subject has been identified through the dominant frequency detection algorithm over the training dataset. Based on the identified RFB, the feature extraction process has been applied to the testing dataset. The estimated 'feature' further classified as per probabilistic Bayesian classifier. The effectiveness of the proposed RFB detection method of electroencephalogram (EEG) signal is validated by self-generated artificial sine wave signal, single subject and nine subject movement imagery (MI) BCI competition dataset. The proposed method of EEG signal processing outperformed the conventional wavelet-based BCI competition II results and the wavelet-based algorithm applied over the BCI competition IV dataset. Keywords: brain-computer interface; BCI; electroencephalogram; EEG; movement imagery; MI; reactive frequency band; RFB; short-time Fourier transforms; STFT; temporal relative spectral power; TRSP; wavelet Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 136 - 152 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091593 Issue No:Vol. 17, No. 1/2 (2018)
Authors:Raghavi K. Bhujang, V. Suma Pages: 153 - 175 Abstract: Risk management is a never ending process in the life cycle of a software project. For efficient risk management, it is necessary to have deep understanding of risk, its types, mitigation strategies so that either the risk is avoidable in future or the impact of the same can be brought down. This paper essentially deals with comprehensive understanding of risk from different perspectives and offers a solution-research methodology for efficient risk management. Risk is prioritised based on the impact that is measured with the help of essential parameters that are inevitable to complete the development cycle of the project. Authors have proposed enhanced risk analysis (ERA) approach to identify measure and calculate the occurrence of risk which can get prioritised. Based on the priority, solution is applied either to mitigate or to bring down the impact of these risks leaning towards the successful completion of the software development project on time. Keywords: risk management; classification of risk; risk mitigation strategies; risk management solution-methodology Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 153 - 175 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091597 Issue No:Vol. 17, No. 1/2 (2018)
Authors:T. Thammi Reddy, B.K. Madhavi, K. Lal Kishore Pages: 176 - 194 Abstract: Dynamic partial reconfiguration (DPR) technique is a very efficient for low-cost field programmable gate array (FPGA) for realising several application categories like signal processing. The present work demonstrates a generic framework for implementing Software Defined Radio (SDR) based communication system using DPR. The work switches contexts with two partial reconfiguration blocks. Namely spectrum estimation and frequency shift keying (FSK) receiver blocks. The former uses the streaming type fast Fourier transform (FFT) and later uses frequency shifting and filtering stages. The completely developed FSK receiver is simulated using Modelsim. Xilinx Zynq 7010 SoC with DPR is used for implementation. An FSK signal with symbol rate 64 Kbps is used to drive the analogue to digital converter (ADC) input. The work demonstrates novel direction for SDR implementation with DPR for low-area FPGAs. The results shows 45% lesser FPGA-DSP48 slices are used compared to without-DPR. Reduced power dissipation is observed as by-product. Keywords: DPR; dynamic partial reconfiguration; FSK generic demodulator; xilinx zynq 7010 soC; DDS; direct digital synthesiser; finite impulse response; FIR filter; Redpitaya Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 176 - 194 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091599 Issue No:Vol. 17, No. 1/2 (2018)
Authors:Amina Ourchani, Zine-Eddine Baarir, Abdelmalik Taleb-Ahmed Pages: 195 - 209 Abstract: Segmentation of moving objects in video sequence is essential task in computer vision. This paper focuses on developing a new method for discriminate moving objects from a static background, focusing on the combination of motion, colour and texture features. First, we have used block-matching for computing the optical flow, we also have taken in consideration the result of frame difference, to improve the quality of the optical flow. Moreover, we have used the k-means clustering algorithm owing to group the pixels, having similar features. Second, the result of the grouping pixels is used as an input in Chan-Vese model, in order to attract the evolving contour of moving objects contours. To evaluate the performance of our proposed method, we experiment it on challenging sequences. It has shown that our method provides an improved segmentation results. Keywords: segmentation; moving object; optical flow; colour feature; texture feature; k-means; frame difference; Chan-Vese model; block-matching; occlusion Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 195 - 209 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091600 Issue No:Vol. 17, No. 1/2 (2018)
Authors:Rohit Garg, Md. Tabrez Nafis, Bindu Garg Pages: 229 - 253 Abstract: In this paper, we have proposed a novel concept to optimise ordered weighted aggregation (OWA) based fuzzy time series predictor (FTSP) using genetic algorithm (GA). Firstly, accurateness of FTSP is enhanced by applying effective method of aggregation on past observations using OWA weights. These weights are determined on the basis of importance of fuzzy set in the system by employing regularly increasing monotonic (RIM) quantifiers. Subsequently, GA is used to optimise membership functions of FTSP by generating its wide range of parameters in the region of time series. Lastly, this model is capable of controlling its performance by varying GA parameters. To assess proposed method, we used dataset of enrolments and outpatient visits, as used by almost all previous research in this domain. Evaluation results indicate coalescing OWA and GA for FTSP significantly reduced mean square error (MSE) and average forecasting error rate (AFER). Keywords: time series analysis; fuzzy logic; GA; genetic algorithm; optimisation; OWA; ordered weighted aggregation Citation: International Journal of Intelligent Systems Technologies and Applications, Vol. 17, No. 1/2 (2018) pp. 229 - 253 PubDate: 2018-05-08T23:20:50-05:00 DOI: 10.1504/IJISTA.2018.091602 Issue No:Vol. 17, No. 1/2 (2018)
Hybrid journal (It can contain Open Access articles)
[447 journals]
